Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "228" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 24 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 24 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2460007 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459999 | RF_maintenance | 0.00% | 0.00% | 0.08% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.6030 | 0.5817 | 0.3076 | nan | nan |
| 2459998 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.518040 | 0.202045 | 0.765822 | -1.223706 | -0.191643 | -0.978051 | 0.824733 | 1.895592 | 0.5685 | 0.5736 | 0.3692 | nan | nan |
| 2459997 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.761710 | 0.519842 | 1.063019 | -1.201315 | 0.549825 | -1.109296 | 0.894726 | 1.441650 | 0.5803 | 0.5882 | 0.3678 | nan | nan |
| 2459996 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 1.221938 | 0.760895 | 0.943929 | -1.345856 | 0.521118 | -1.008016 | 3.321271 | 1.217071 | 0.5868 | 0.5936 | 0.3787 | nan | nan |
| 2459995 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.960210 | -0.362504 | 1.015914 | -1.490809 | 0.343041 | -0.956320 | 0.284369 | 0.548331 | 0.5864 | 0.5931 | 0.3738 | nan | nan |
| 2459994 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.941922 | -0.092650 | 0.997480 | -1.368646 | 0.236689 | -0.791726 | -0.197156 | 0.904074 | 0.5807 | 0.5838 | 0.3695 | nan | nan |
| 2459993 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 1.240999 | -0.233126 | 1.239631 | -1.265593 | 1.074380 | -1.050783 | -0.303305 | 0.460101 | 0.5724 | 0.5858 | 0.3783 | nan | nan |
| 2459991 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 1.046935 | -0.089279 | 1.181137 | -1.282900 | 0.712887 | -1.052655 | 0.087819 | 1.028085 | 0.5817 | 0.5761 | 0.3790 | nan | nan |
| 2459990 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.908949 | -0.040236 | 1.193957 | -1.222273 | 0.700799 | -1.043551 | -0.399680 | 0.627239 | 0.5805 | 0.5777 | 0.3743 | nan | nan |
| 2459989 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.813039 | 0.009187 | 1.286518 | -1.101750 | 0.607855 | -1.148926 | -0.274287 | 0.707586 | 0.5785 | 0.5793 | 0.3764 | nan | nan |
| 2459988 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.863848 | -0.126968 | 1.224708 | -1.263560 | 0.936495 | -1.101168 | -0.227214 | 0.586144 | 0.5789 | 0.5821 | 0.3714 | nan | nan |
| 2459987 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.629655 | -0.280841 | 1.073643 | -1.377693 | 0.122817 | -0.923791 | -0.207019 | 1.014063 | 0.5862 | 0.5893 | 0.3664 | nan | nan |
| 2459986 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 1.084300 | -0.171169 | 1.207282 | -1.323719 | 0.527198 | -1.371386 | 0.513099 | -0.880466 | 0.6118 | 0.6223 | 0.3245 | nan | nan |
| 2459985 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.820036 | -0.286390 | 0.984379 | -1.405959 | 0.215859 | -1.145110 | 1.178076 | 1.405305 | 0.5880 | 0.5914 | 0.3750 | nan | nan |
| 2459984 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.679849 | -0.468267 | 1.072851 | -1.345977 | 0.382808 | -1.667010 | 0.191038 | -0.629326 | 0.6000 | 0.6087 | 0.3564 | nan | nan |
| 2459983 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.361680 | -0.169035 | 1.113704 | -1.187572 | 0.031202 | -0.698306 | -0.571277 | -0.447189 | 0.6081 | 0.6197 | 0.3315 | nan | nan |
| 2459982 | RF_maintenance | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.871237 | -0.186130 | 0.490755 | -1.125801 | -1.227542 | -0.885247 | -0.746177 | -0.978133 | 0.6775 | 0.6749 | 0.2862 | nan | nan |
| 2459981 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 6.977158 | 18.393779 | -0.860536 | -0.005022 | 1.604616 | 6.316070 | 50.872663 | 46.430795 | 0.5241 | 0.4948 | 0.3144 | nan | nan |
| 2459980 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 6.674144 | 18.626255 | -0.874283 | -0.465505 | 1.505530 | 3.104370 | 5.051734 | 3.616699 | 0.5878 | 0.5557 | 0.2507 | nan | nan |
| 2459979 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 7.281839 | 19.680941 | -0.958817 | -0.434204 | 1.227142 | 2.042108 | 44.493005 | 12.527510 | 0.5179 | 0.4902 | 0.3113 | nan | nan |
| 2459978 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 5.986215 | 20.276446 | -1.014809 | -0.353662 | 0.404486 | 3.400990 | 14.962790 | 27.329670 | 0.5276 | 0.4921 | 0.3200 | nan | nan |
| 2459977 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 9.795298 | 21.345415 | -0.684666 | -0.654795 | 5.338093 | 3.900429 | 114.896610 | 26.876407 | 0.4586 | 0.4257 | 0.2485 | nan | nan |
| 2459976 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 4.818371 | 21.711999 | -0.865145 | -0.565234 | 0.865720 | 3.259141 | 24.964248 | 9.032601 | 0.5349 | 0.4883 | 0.3048 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 228 | N20 | RF_maintenance | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 228 | N20 | RF_maintenance | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 228 | N20 | RF_maintenance | nn Temporal Discontinuties | 1.895592 | 0.518040 | 0.202045 | 0.765822 | -1.223706 | -0.191643 | -0.978051 | 0.824733 | 1.895592 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 228 | N20 | RF_maintenance | nn Temporal Discontinuties | 1.441650 | 0.761710 | 0.519842 | 1.063019 | -1.201315 | 0.549825 | -1.109296 | 0.894726 | 1.441650 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 228 | N20 | RF_maintenance | ee Temporal Discontinuties | 3.321271 | 1.221938 | 0.760895 | 0.943929 | -1.345856 | 0.521118 | -1.008016 | 3.321271 | 1.217071 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 228 | N20 | RF_maintenance | ee Power | 1.015914 | 0.960210 | -0.362504 | 1.015914 | -1.490809 | 0.343041 | -0.956320 | 0.284369 | 0.548331 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 228 | N20 | RF_maintenance | ee Power | 0.997480 | 0.941922 | -0.092650 | 0.997480 | -1.368646 | 0.236689 | -0.791726 | -0.197156 | 0.904074 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 228 | N20 | RF_maintenance | ee Shape | 1.240999 | 1.240999 | -0.233126 | 1.239631 | -1.265593 | 1.074380 | -1.050783 | -0.303305 | 0.460101 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 228 | N20 | RF_maintenance | ee Power | 1.181137 | 1.046935 | -0.089279 | 1.181137 | -1.282900 | 0.712887 | -1.052655 | 0.087819 | 1.028085 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 228 | N20 | RF_maintenance | ee Power | 1.193957 | -0.040236 | 0.908949 | -1.222273 | 1.193957 | -1.043551 | 0.700799 | 0.627239 | -0.399680 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 228 | N20 | RF_maintenance | ee Power | 1.286518 | 0.009187 | 0.813039 | -1.101750 | 1.286518 | -1.148926 | 0.607855 | 0.707586 | -0.274287 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 228 | N20 | RF_maintenance | ee Power | 1.224708 | -0.126968 | 0.863848 | -1.263560 | 1.224708 | -1.101168 | 0.936495 | 0.586144 | -0.227214 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 228 | N20 | RF_maintenance | ee Power | 1.073643 | 0.629655 | -0.280841 | 1.073643 | -1.377693 | 0.122817 | -0.923791 | -0.207019 | 1.014063 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 228 | N20 | RF_maintenance | ee Power | 1.207282 | -0.171169 | 1.084300 | -1.323719 | 1.207282 | -1.371386 | 0.527198 | -0.880466 | 0.513099 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 228 | N20 | RF_maintenance | nn Temporal Discontinuties | 1.405305 | -0.286390 | 0.820036 | -1.405959 | 0.984379 | -1.145110 | 0.215859 | 1.405305 | 1.178076 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 228 | N20 | RF_maintenance | ee Power | 1.072851 | 0.679849 | -0.468267 | 1.072851 | -1.345977 | 0.382808 | -1.667010 | 0.191038 | -0.629326 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 228 | N20 | RF_maintenance | ee Power | 1.113704 | 0.361680 | -0.169035 | 1.113704 | -1.187572 | 0.031202 | -0.698306 | -0.571277 | -0.447189 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 228 | N20 | RF_maintenance | ee Power | 0.490755 | -0.871237 | -0.186130 | 0.490755 | -1.125801 | -1.227542 | -0.885247 | -0.746177 | -0.978133 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 228 | N20 | RF_maintenance | ee Temporal Discontinuties | 50.872663 | 18.393779 | 6.977158 | -0.005022 | -0.860536 | 6.316070 | 1.604616 | 46.430795 | 50.872663 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 228 | N20 | RF_maintenance | nn Shape | 18.626255 | 18.626255 | 6.674144 | -0.465505 | -0.874283 | 3.104370 | 1.505530 | 3.616699 | 5.051734 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 228 | N20 | RF_maintenance | ee Temporal Discontinuties | 44.493005 | 7.281839 | 19.680941 | -0.958817 | -0.434204 | 1.227142 | 2.042108 | 44.493005 | 12.527510 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 228 | N20 | RF_maintenance | nn Temporal Discontinuties | 27.329670 | 20.276446 | 5.986215 | -0.353662 | -1.014809 | 3.400990 | 0.404486 | 27.329670 | 14.962790 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 228 | N20 | RF_maintenance | ee Temporal Discontinuties | 114.896610 | 9.795298 | 21.345415 | -0.684666 | -0.654795 | 5.338093 | 3.900429 | 114.896610 | 26.876407 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 228 | N20 | RF_maintenance | ee Temporal Discontinuties | 24.964248 | 21.711999 | 4.818371 | -0.565234 | -0.865145 | 3.259141 | 0.865720 | 9.032601 | 24.964248 |